Development of Digital Twin for FDM Printer With Preventive Cyber-Attack and Control Algorithms
Open accessAbstract
This paper presents a developed model of a Digital Twin (DT) for a fused deposition modeling (FDM) printer, real-time defect detection, and proposed frameworks for preventing cyber-attacks in real-time. It also highlights a model predictive control (MPC) algorithm based on a real-time feedback system for controlling the material feed. The system is designed and developed based on DT, and MPC with integrated machine learning (ML) algorithms to establish real-time process control and enhance the safety and reliability of the physical plant. ML algorithm is used for anomaly detection based on the convolutional neural network (CNN) model. The developed system can be practically utilized in smart manufacturing industries as well as cyber-physical systems-based plants. The work is novel and original as this type of DT and cyber-physical systems (CPS) are very new to additive manufacturing (AM) industries. There are several conceptual models in the literature and there is a critical need for such implemented working systems.
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Kim et al. [127] achieve real-time synchronization between the physical twin (PT) and digital twin (DT), collecting and analyzing control commands and sensor feedback simultaneously to detect cyber-physical anomalies in the printing process.
“The authors achieve real-time synchronization between the physical twin (PT) and digital twin (DT), collecting and analyzing control commands and sensor feedback simultaneously to detect cyber-physical anomalies in the printing process.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [127]
Kim et al. [127]'s dual control scheme integrated with a PLC stops the machine and restores it to a safe state when anomaly patterns are detected, realizing preventive control.
“Furthermore, a dual control scheme integrated with a PLC stops the machine and restores it to a safe state when anomaly patterns are detected, realizing preventive control.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [127]
Kim et al. [127]'s proposed model is trained and validated on the custom DT-PT dataset and represents a hybrid anomaly detection architecture that simultaneously exploits control, sensor, and process image data.
“The proposed model is trained and validated on the custom DT-PT dataset and represents a hybrid anomaly detection architecture that simultaneously exploits control, sensor, and process image data.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [127]
In Kim et al. [127]'s detection phase, DBSCAN-based clustering is combined with an EfficientDetLite deep model to identify nozzle position errors, temperature deviations, and vibration anomalies.
“In the detection phase, DBSCAN-based clustering is combined with an EfficientDetLite deep model to identify nozzle position errors, temperature deviations, and vibration anomalies.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [127]
Kim et al. [127] proposed a data fusion-based control algorithm that jointly performs cyberattack prevention and anomaly detection using an FDM 3D printer digital twin dataset constructed in-house.
“Kim et al. [127] proposed a data fusion-based control algorithm that jointly performs cyberattack prevention and anomaly detection using an FDM 3D printer digital twin (DT) dataset constructed in-house.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [127]
Many hybrid data fusion-based anomaly detection approaches explicitly synchronize cyber-layer events with physical-layer behavior on a common time axis to detect cyber-physical inconsistencies that single-domain detectors would miss.
“First, many approaches explicitly synchronize cyber-layer events with physical-layer behavior on a common time axis to detect cyber-physical inconsistencies that single-domain detectors would miss [122-129].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [127]
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- Authors
- Hazrat Ali, M., Waqar Malik, A., Jyeniskhan, N., Arif Mahmood, M., Shehab, E., & Liou, F.
- Affiliations
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, Kazakhstan
- Department of Electrical and Computer Engineering, National Strategic Planning and Analysis Research Center (NSPARC), Mississippi State University, Starkville, MS, USA
- University of Stavanger (NO)
- Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO, USA
- Nazarbayev University (KZ)
- Missouri University of Science and Technology (US)
- ORCID
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 193594-193606
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4405755418
- Cited by
- 12
- References
- 30
- Citations by year
- 2026: 9 · 2025: 3
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Nazarbayev University through the Faculty Development Competitive Research Grant Program (11022021FD2904)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, fused deposition modeling, 3D printer, cyberattack prevention, model predictive control, real-time defect detection, convolutional neural networks, anomaly detection, cyber-physical systems, additive manufacturing, smart manufacturing
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